An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems

• Published in: TheScientificWorld Vol. 2016; no. 2016; pp. 1 - 14
• Main Authors: Ranganayaki, V., Deepa, S. N.
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2016; Hindawi Limited; Wiley
• Subjects: Accuracy; Alternative energy sources; Artificial neural networks; Mathematical models; Measurement; Neural networks; Neurons; Statistical methods; Technology application; Wind farms; Wind power; Wind velocity
• ISSN: 2356-6140; 1537-744X; 1537-744X
• DOI: 10.1155/2016/9293529

Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature.